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A key property of linguistic conventions is that they hold over an entirecommunity of speakers, allowing us to communicate efficiently even with peoplewe have never met before. At the same time, much of our language use ispartner-specific: we know that words may be understood differently by differentpeople based on our shared history. This poses a challenge for accounts ofconvention formation. Exactly how do agents make the inferential leap tocommunity-wide expectations while maintaining partner-specific knowledge? Wepropose a hierarchical Bayesian model to explain how speakers and listenerssolve this inductive problem. To evaluate our model's predictions, we conductedan experiment where participants played an extended natural-languagecommunication game with different partners in a small community. We examineseveral measures of generalization and find key signatures of bothpartner-specificity and community convergence that distinguish our model fromalternatives. These results suggest that partner-specificity is not onlycompatible with the formation of community-wide conventions, but may facilitateit when coupled with a powerful inductive mechanism.

Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks